Redo high_five movie with correlate.py, do tank_battle movie with correlate.py, rough...

[stop_motion.git] / correlate.py

diff --git a/correlate.py b/correlate.py
index 9162c67..d8ae5cc 100755 (executable)
--- a/correlate.py
+++ b/correlate.py
@@ -96,7 +96,7 @@ def calc_match(normalized0, normalized1, xc, yc):
       max(yo - 1, 0):max(yo + 2, 0),
       max(xo - 21, 0):max(xo + 22, 0)
     ] = 0.
-    gamma.write_image(f'diag_{xc:d}_{yc:d}_corr.jpg', temp)
+    gamma.write_image(f'diag_{xc:d}_{yc:d}_corr.png', temp)
   if (
     max_corr < EPSILON or
       xo < CUTOFF1 or
@@ -130,7 +130,7 @@ def calc_match(normalized0, normalized1, xc, yc):
       max(y - 1, 0):max(y + 2, 0),
       max(x - 21, 0):max(x + 22, 0)
     ] = 0.
-    gamma.write_image(f'diag_{xc:d}_{yc:d}_match.jpg', temp)
+    gamma.write_image(f'diag_{xc:d}_{yc:d}_match.png', temp)
 
     diag0 = block0 + .5
     diag0[
@@ -143,7 +143,7 @@ def calc_match(normalized0, normalized1, xc, yc):
       max(x - 21, 0):max(x + 22, 0),
       :
     ] = 0.
-    gamma.write_image(f'diag_{xc:d}_{yc:d}_block0.jpg', diag0)
+    gamma.write_image(f'diag_{xc:d}_{yc:d}_block0.png', diag0)
 
     diag1 = block10 + .5
     diag1[
@@ -156,7 +156,7 @@ def calc_match(normalized0, normalized1, xc, yc):
       max(x - 21, 0):max(x + 22, 0),
       :
     ] = 0.
-    gamma.write_image(f'diag_{xc:d}_{yc:d}_block10.jpg', diag1)
+    gamma.write_image(f'diag_{xc:d}_{yc:d}_block10.png', diag1)
 
     x += xo
     y += yo
@@ -171,7 +171,7 @@ def calc_match(normalized0, normalized1, xc, yc):
       max(x - 21, 0):max(x + 22, 0),
       :
     ] = 0.
-    gamma.write_image(f'diag_{xc:d}_{yc:d}_block11.jpg', diag1)
+    gamma.write_image(f'diag_{xc:d}_{yc:d}_block11.png', diag1)
 
   # return offset and feature relative to block centre
   return xo - XS, yo - YS, xf - XM // 2, yf - YM // 2
@@ -202,14 +202,14 @@ files = [
 ]
 
 # first file is special as no transformation needs to be done
-in_jpg, out_jpg = files[0]
+in_png, out_png = files[0]
 
-print(f'read {in_jpg:s}')
-image0 = gamma.read_image(in_jpg)
+print(f'read {in_png:s}')
+image0 = gamma.read_image(in_png)
 shape = image0.shape
 
-sys.stderr.write(f'write {out_jpg:s}\n')
-gamma.write_image(out_jpg, image0)
+sys.stderr.write(f'write {out_png:s}\n')
+gamma.write_image(out_png, image0)
 
 ys, xs, cs = shape
 xb = (xs - XM - 2 * XS) // XP // 2
@@ -219,21 +219,21 @@ print('xb', xb, 'yb', yb)
 print('normalize')
 normalized0 = normalize(image0)
 if diag:
-  gamma.write_image('normalized0.jpg', normalized0 + .5)
+  gamma.write_image('normalized0.png', normalized0 + .5)
 
 # loop through remaining files comparing each to the previous
 cumulative_A = numpy.identity(3, numpy.double)
 for file in range(1, len(files)):
-  in_jpg, out_jpg = files[file]
+  in_png, out_png = files[file]
 
-  print(f'read {in_jpg:s}')
-  image1 = gamma.read_image(in_jpg)
+  print(f'read {in_png:s}')
+  image1 = gamma.read_image(in_png)
   assert image1.shape == shape
 
   print('normalize')
   normalized1 = normalize(image1)
   if diag:
-    gamma.write_image(f'normalized{file:d}.jpg', normalized1 + .5)
+    gamma.write_image(f'normalized{file:d}.png', normalized1 + .5)
 
   print('find corner candidates')
   p_all = []
@@ -351,14 +351,14 @@ for file in range(1, len(files)):
           max(xf1 - 21, 0):max(xf1 + 22, 0),
           :
         ] = 0.
-    gamma.write_image(f'diag_file{file:d}_0.jpg', diag0)
-    gamma.write_image(f'diag_file{file:d}_1.jpg', diag1)
+    gamma.write_image(f'diag_file{file:d}_0.png', diag0)
+    gamma.write_image(f'diag_file{file:d}_1.png', diag1)
 
   print('remap')
   out_image1 = perspective.remap_image(cumulative_A, image1)
 
-  sys.stderr.write(f'write {out_jpg:s}\n')
-  gamma.write_image(out_jpg, out_image1)
+  sys.stderr.write(f'write {out_png:s}\n')
+  gamma.write_image(out_png, out_image1)
 
   image0 = image1
   normalized0 = normalized1